1. Field of the Invention
The present invention relates to quality evaluation in general, and more specifically to a method and system for automatic quality assessment of performance in an organization.
2. Discussion of the Related Art
Quality evaluation tools are intended for obtaining, recording or using productivity, quality or performance measures within an organization. Within organizations or organizations' units that mainly handle customer interactions, such as call centers, customer relations centers, trade floors or the like, a key factor is quality monitoring of various elements, such as the proficiency of personnel member interacting with calling parties, the impact of a campaign, the success of a product sale or a product, especially in relation to the competition, or the like. An agent interacting with a customer represents the organization to that customer, and is responsible for a significant part of the customer experience. A pleasant and professional agent can prove useful in customer service and customer retention as well as in influencing new customers to buy services or goods from the organization. On the other hand, agents are a resource of the organization, and as such their time should be managed as efficiently as possible. Thus, there is great importance in evaluating the agents' performance on a regular basis, for purposes such as identifying and correcting inefficiencies in an agent's conduct, rewarding agents for notable performance, or the like.
Traditionally, evaluations are done by an evaluator using an evaluation tool. In a typical call center service evaluation scenario, a supervisor listens to a randomly selected call of a specific agent, fills in an evaluation form, and attributes to the agent or to the call a quality score or other scores and indications. During employee evaluation processes or if significant deficiencies are detected in the agent's performance, the supervisor may talk to the agent, suggest a training session or take other measures. The scores assigned to a call may be taken into account when evaluating or analyzing a campaign, a product, a product line or the like.
The traditional evaluation scheme described above has multiple deficiencies. First, the evaluation capacity is relatively low due to the dependence of the evaluation process on the human evaluator. Next, the scope of the evaluation may be limited due to the range of factors that can be taken into account when evaluating an interaction, including the captured interaction itself the agent's workload, the call center workload during the interaction time and its impact on the service quality (e.g. queue time before agent availability), the history of interactions between the agent and the specific customer, the contribution of other agents to an activity involving several agents, the details and behavior profile of the specific customer and the like. Human evaluators may not be aware or capable of considering such factors which may be relevant to the interaction quality and its evaluation. Another limitation is that the overall evaluation may be biased due to the relatively small number of the interactions that can be evaluated using current techniques and methodologies. Thus, the evaluator typically samples a fraction of the interactions made by all agent as a basis for the evaluation, which may be non-representing and may not indicate important issues. Yet another problem is that there is no mechanism that can identify evaluation-worthy interactions and prioritize the interactions for evaluation. In addition, the evaluation may be subjective and biased due to the dependence on the specific agent and evaluator involved, and possibly their relationship. Moreover, the evaluator may not be aware of this bias. Also, the evaluation is executed post activity and by another person. Thus, factors that can influence the quality of the interaction (e.g. a customer has waited a long time on queue before the activity) may be unknown to the evaluator at the time of evaluation. Yet another problem is that evaluations are based on evaluating the activity itself and do not incorporate external factors such as the customer's satisfaction, as part of the quality evaluation. Moreover, no use or little use is done in parameters that can be drawn from the interactions and can be used for calibrating business processes and policies (e.g. the relation between the interaction's quality and its duration, or the relation between queue time before the interaction and the customer satisfaction when available). Evaluations can be further used for other agent related activities, such as recruitment (e.g. what is the predicted quality of a candidate agent, based on his background and skills profile), promotion and compensation (i.e. the objective quality of the agent) and retention (the relation between the agent's quality trend and the agent's probability to leave). When employing quality monitoring, it is desired that outstanding interactions are notified to a supervisor, or another person within the organization. It is also desired that real-time or near-real-time alert is generated for such interactions, or agent quality trend where there might be room for effective reparative intervention.
These is therefore a need in the art for a system and apparatus for automated quality monitoring, which will overcome the problems and disadvantages of prior art systems and of manual evaluation methods. The solution should provide more characteristics, take into account more factors, and make the evaluation results available to additional tools and systems intended for improving the performance of the organization or parts thereof.